{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 前言"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "豆瓣top250榜单进行分析，看都有哪些电影，评分怎么样，电影的类型都有哪些，电影上映日期分布是怎样的，哪个国家/地区发行的电影比较多。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:lml.utils:failed to import pyecharts_snapshot\n",
      "Traceback (most recent call last):\n",
      "  File \"D:\\Program Files (x86)\\Anaconda3\\lib\\site-packages\\lml\\utils.py\", line 43, in do_import\n",
      "    plugin_module = __import__(plugin_module_name)\n",
      "ModuleNotFoundError: No module named 'pyecharts_snapshot'\n",
      "\n",
      "Bad key \"text.kerning_factor\" on line 4 in\n",
      "D:\\Program Files (x86)\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test_patch.mplstyle.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "# 导入常用库\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import pyecharts as pe\n",
    "import collections\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 图表可以显示中文\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据概览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>电影名称</th>\n",
       "      <th>电影其他信息</th>\n",
       "      <th>导演</th>\n",
       "      <th>编剧</th>\n",
       "      <th>主演</th>\n",
       "      <th>类型</th>\n",
       "      <th>制片国家/地区</th>\n",
       "      <th>语言</th>\n",
       "      <th>上映日期</th>\n",
       "      <th>片长</th>\n",
       "      <th>又名</th>\n",
       "      <th>IMDb链接</th>\n",
       "      <th>评分</th>\n",
       "      <th>评分人数</th>\n",
       "      <th>官方网站</th>\n",
       "      <th>官方小站</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>肖申克的救赎 The Shawshank Redemption</td>\n",
       "      <td>['', '导演:弗兰克·德拉邦特', '编剧:弗兰克·德拉邦特/斯蒂芬·金', '主演:蒂...</td>\n",
       "      <td>弗兰克·德拉邦特</td>\n",
       "      <td>弗兰克·德拉邦特/斯蒂芬·金</td>\n",
       "      <td>蒂姆·罗宾斯/摩根·弗里曼/鲍勃·冈顿/威廉姆·赛德勒/克兰西·布朗/吉尔·贝罗斯/马克·罗...</td>\n",
       "      <td>剧情/犯罪</td>\n",
       "      <td>美国</td>\n",
       "      <td>英语</td>\n",
       "      <td>1994-09-10(多伦多电影节)/1994-10-14(美国)</td>\n",
       "      <td>142分钟</td>\n",
       "      <td>月黑高飞(港)/刺激1995(台)/地狱诺言/铁窗岁月/消香克的救赎</td>\n",
       "      <td>tt0111161</td>\n",
       "      <td>9.7</td>\n",
       "      <td>2100927</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>霸王别姬</td>\n",
       "      <td>['', '导演:陈凯歌', '编剧:芦苇/李碧华', '主演:张国荣/张丰毅/巩俐/葛优/...</td>\n",
       "      <td>陈凯歌</td>\n",
       "      <td>芦苇/李碧华</td>\n",
       "      <td>张国荣/张丰毅/巩俐/葛优/英达/蒋雯丽/吴大维/吕齐/雷汉/尹治/马明威/费振翔/智一桐/...</td>\n",
       "      <td>剧情/爱情/同性</td>\n",
       "      <td>中国大陆/中国香港</td>\n",
       "      <td>汉语普通话</td>\n",
       "      <td>1993-01-01(中国香港)/1993-07-26(中国大陆)</td>\n",
       "      <td>171分钟/155分钟(美国剧场版)</td>\n",
       "      <td>再见，我的妾/FarewellMyConcubine</td>\n",
       "      <td>tt0106332</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1557983</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>阿甘正传 Forrest Gump</td>\n",
       "      <td>['', '导演:罗伯特·泽米吉斯', '编剧:艾瑞克·罗斯/温斯顿·格鲁姆', '主演:汤...</td>\n",
       "      <td>罗伯特·泽米吉斯</td>\n",
       "      <td>艾瑞克·罗斯/温斯顿·格鲁姆</td>\n",
       "      <td>汤姆·汉克斯/罗宾·怀特/加里·西尼斯/麦凯尔泰·威廉逊/莎莉·菲尔德/海利·乔·奥斯蒙/迈...</td>\n",
       "      <td>剧情/爱情</td>\n",
       "      <td>美国</td>\n",
       "      <td>英语</td>\n",
       "      <td>1994-06-23(洛杉矶首映)/1994-07-06(美国)</td>\n",
       "      <td>142分钟</td>\n",
       "      <td>福雷斯特·冈普</td>\n",
       "      <td>tt0109830</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1587792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>这个杀手不太冷 Léon</td>\n",
       "      <td>['', '导演:吕克·贝松', '编剧:吕克·贝松', '主演:让·雷诺/娜塔莉·波特曼/...</td>\n",
       "      <td>吕克·贝松</td>\n",
       "      <td>吕克·贝松</td>\n",
       "      <td>让·雷诺/娜塔莉·波特曼/加里·奥德曼/丹尼·爱罗/彼得·阿佩尔/迈克尔·巴达鲁科/艾伦·格...</td>\n",
       "      <td>剧情/动作/犯罪</td>\n",
       "      <td>法国/美国</td>\n",
       "      <td>英语/意大利语/法语</td>\n",
       "      <td>1994-09-14(法国)</td>\n",
       "      <td>110分钟(剧场版)/133分钟(国际版)</td>\n",
       "      <td>杀手莱昂/终极追杀令(台)/杀手里昂/Leon/Leon</td>\n",
       "      <td>tt0110413</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1775679</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>泰坦尼克号 Titanic</td>\n",
       "      <td>['', '导演:詹姆斯·卡梅隆', '编剧:詹姆斯·卡梅隆', '主演:莱昂纳多·迪卡普里...</td>\n",
       "      <td>詹姆斯·卡梅隆</td>\n",
       "      <td>詹姆斯·卡梅隆</td>\n",
       "      <td>莱昂纳多·迪卡普里奥/凯特·温丝莱特/比利·赞恩/凯西·贝茨/弗兰西丝·费舍/格劳瑞亚·斯图...</td>\n",
       "      <td>剧情/爱情/灾难</td>\n",
       "      <td>美国</td>\n",
       "      <td>英语/意大利语/德语/俄语</td>\n",
       "      <td>1998-04-03(中国大陆)/1997-11-01(东京电影节)/1997-12-19(美国)</td>\n",
       "      <td>194分钟/227分钟(白星版)</td>\n",
       "      <td>铁达尼号(港/台)</td>\n",
       "      <td>tt0120338</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1540577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                             电影名称  \\\n",
       "0           0  肖申克的救赎 The Shawshank Redemption   \n",
       "1           1                             霸王别姬   \n",
       "2           2                阿甘正传 Forrest Gump   \n",
       "3           3                     这个杀手不太冷 Léon   \n",
       "4           4                    泰坦尼克号 Titanic   \n",
       "\n",
       "                                              电影其他信息        导演  \\\n",
       "0  ['', '导演:弗兰克·德拉邦特', '编剧:弗兰克·德拉邦特/斯蒂芬·金', '主演:蒂...  弗兰克·德拉邦特   \n",
       "1  ['', '导演:陈凯歌', '编剧:芦苇/李碧华', '主演:张国荣/张丰毅/巩俐/葛优/...       陈凯歌   \n",
       "2  ['', '导演:罗伯特·泽米吉斯', '编剧:艾瑞克·罗斯/温斯顿·格鲁姆', '主演:汤...  罗伯特·泽米吉斯   \n",
       "3  ['', '导演:吕克·贝松', '编剧:吕克·贝松', '主演:让·雷诺/娜塔莉·波特曼/...     吕克·贝松   \n",
       "4  ['', '导演:詹姆斯·卡梅隆', '编剧:詹姆斯·卡梅隆', '主演:莱昂纳多·迪卡普里...   詹姆斯·卡梅隆   \n",
       "\n",
       "               编剧                                                 主演  \\\n",
       "0  弗兰克·德拉邦特/斯蒂芬·金  蒂姆·罗宾斯/摩根·弗里曼/鲍勃·冈顿/威廉姆·赛德勒/克兰西·布朗/吉尔·贝罗斯/马克·罗...   \n",
       "1          芦苇/李碧华  张国荣/张丰毅/巩俐/葛优/英达/蒋雯丽/吴大维/吕齐/雷汉/尹治/马明威/费振翔/智一桐/...   \n",
       "2  艾瑞克·罗斯/温斯顿·格鲁姆  汤姆·汉克斯/罗宾·怀特/加里·西尼斯/麦凯尔泰·威廉逊/莎莉·菲尔德/海利·乔·奥斯蒙/迈...   \n",
       "3           吕克·贝松  让·雷诺/娜塔莉·波特曼/加里·奥德曼/丹尼·爱罗/彼得·阿佩尔/迈克尔·巴达鲁科/艾伦·格...   \n",
       "4         詹姆斯·卡梅隆  莱昂纳多·迪卡普里奥/凯特·温丝莱特/比利·赞恩/凯西·贝茨/弗兰西丝·费舍/格劳瑞亚·斯图...   \n",
       "\n",
       "         类型    制片国家/地区             语言  \\\n",
       "0     剧情/犯罪         美国             英语   \n",
       "1  剧情/爱情/同性  中国大陆/中国香港          汉语普通话   \n",
       "2     剧情/爱情         美国             英语   \n",
       "3  剧情/动作/犯罪      法国/美国     英语/意大利语/法语   \n",
       "4  剧情/爱情/灾难         美国  英语/意大利语/德语/俄语   \n",
       "\n",
       "                                                上映日期                     片长  \\\n",
       "0                  1994-09-10(多伦多电影节)/1994-10-14(美国)                  142分钟   \n",
       "1                  1993-01-01(中国香港)/1993-07-26(中国大陆)     171分钟/155分钟(美国剧场版)   \n",
       "2                   1994-06-23(洛杉矶首映)/1994-07-06(美国)                  142分钟   \n",
       "3                                     1994-09-14(法国)  110分钟(剧场版)/133分钟(国际版)   \n",
       "4  1998-04-03(中国大陆)/1997-11-01(东京电影节)/1997-12-19(美国)       194分钟/227分钟(白星版)   \n",
       "\n",
       "                                   又名     IMDb链接   评分     评分人数 官方网站  官方小站  \n",
       "0  月黑高飞(港)/刺激1995(台)/地狱诺言/铁窗岁月/消香克的救赎  tt0111161  9.7  2100927  NaN   NaN  \n",
       "1          再见，我的妾/FarewellMyConcubine  tt0106332  9.6  1557983  NaN   NaN  \n",
       "2                             福雷斯特·冈普  tt0109830  9.5  1587792  NaN   NaN  \n",
       "3        杀手莱昂/终极追杀令(台)/杀手里昂/Leon/Leon  tt0110413  9.4  1775679  NaN   NaN  \n",
       "4                           铁达尼号(港/台)  tt0120338  9.4  1540577  NaN   NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取 Excel 数据\n",
    "df = pd.read_excel('豆瓣电影top250.xlsx')\n",
    "df.head()  # 查看前5行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', '电影名称', '电影其他信息', '导演', '编剧', '主演', '类型', '制片国家/地区', '语言',\n",
       "       '上映日期', '片长', '又名', 'IMDb链接', '评分', '评分人数', '官方网站', '官方小站'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取数据列名\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>电影名称</th>\n",
       "      <th>导演</th>\n",
       "      <th>主演</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>肖申克的救赎 The Shawshank Redemption</td>\n",
       "      <td>弗兰克·德拉邦特</td>\n",
       "      <td>蒂姆·罗宾斯/摩根·弗里曼/鲍勃·冈顿/威廉姆·赛德勒/克兰西·布朗/吉尔·贝罗斯/马克·罗...</td>\n",
       "      <td>剧情/犯罪</td>\n",
       "      <td>美国</td>\n",
       "      <td>1994-09-10(多伦多电影节)/1994-10-14(美国)</td>\n",
       "      <td>142分钟</td>\n",
       "      <td>9.7</td>\n",
       "      <td>2100927</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>霸王别姬</td>\n",
       "      <td>陈凯歌</td>\n",
       "      <td>张国荣/张丰毅/巩俐/葛优/英达/蒋雯丽/吴大维/吕齐/雷汉/尹治/马明威/费振翔/智一桐/...</td>\n",
       "      <td>剧情/爱情/同性</td>\n",
       "      <td>中国大陆/中国香港</td>\n",
       "      <td>1993-01-01(中国香港)/1993-07-26(中国大陆)</td>\n",
       "      <td>171分钟/155分钟(美国剧场版)</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1557983</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>阿甘正传 Forrest Gump</td>\n",
       "      <td>罗伯特·泽米吉斯</td>\n",
       "      <td>汤姆·汉克斯/罗宾·怀特/加里·西尼斯/麦凯尔泰·威廉逊/莎莉·菲尔德/海利·乔·奥斯蒙/迈...</td>\n",
       "      <td>剧情/爱情</td>\n",
       "      <td>美国</td>\n",
       "      <td>1994-06-23(洛杉矶首映)/1994-07-06(美国)</td>\n",
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       "      <td>9.5</td>\n",
       "      <td>1587792</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>这个杀手不太冷 Léon</td>\n",
       "      <td>吕克·贝松</td>\n",
       "      <td>让·雷诺/娜塔莉·波特曼/加里·奥德曼/丹尼·爱罗/彼得·阿佩尔/迈克尔·巴达鲁科/艾伦·格...</td>\n",
       "      <td>剧情/动作/犯罪</td>\n",
       "      <td>法国/美国</td>\n",
       "      <td>1994-09-14(法国)</td>\n",
       "      <td>110分钟(剧场版)/133分钟(国际版)</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1775679</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>泰坦尼克号 Titanic</td>\n",
       "      <td>詹姆斯·卡梅隆</td>\n",
       "      <td>莱昂纳多·迪卡普里奥/凯特·温丝莱特/比利·赞恩/凯西·贝茨/弗兰西丝·费舍/格劳瑞亚·斯图...</td>\n",
       "      <td>剧情/爱情/灾难</td>\n",
       "      <td>美国</td>\n",
       "      <td>1998-04-03(中国大陆)/1997-11-01(东京电影节)/1997-12-19(美国)</td>\n",
       "      <td>194分钟/227分钟(白星版)</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1540577</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              电影名称        导演  \\\n",
       "0  肖申克的救赎 The Shawshank Redemption  弗兰克·德拉邦特   \n",
       "1                             霸王别姬       陈凯歌   \n",
       "2                阿甘正传 Forrest Gump  罗伯特·泽米吉斯   \n",
       "3                     这个杀手不太冷 Léon     吕克·贝松   \n",
       "4                    泰坦尼克号 Titanic   詹姆斯·卡梅隆   \n",
       "\n",
       "                                                  主演        类型    制片国家/地区  \\\n",
       "0  蒂姆·罗宾斯/摩根·弗里曼/鲍勃·冈顿/威廉姆·赛德勒/克兰西·布朗/吉尔·贝罗斯/马克·罗...     剧情/犯罪         美国   \n",
       "1  张国荣/张丰毅/巩俐/葛优/英达/蒋雯丽/吴大维/吕齐/雷汉/尹治/马明威/费振翔/智一桐/...  剧情/爱情/同性  中国大陆/中国香港   \n",
       "2  汤姆·汉克斯/罗宾·怀特/加里·西尼斯/麦凯尔泰·威廉逊/莎莉·菲尔德/海利·乔·奥斯蒙/迈...     剧情/爱情         美国   \n",
       "3  让·雷诺/娜塔莉·波特曼/加里·奥德曼/丹尼·爱罗/彼得·阿佩尔/迈克尔·巴达鲁科/艾伦·格...  剧情/动作/犯罪      法国/美国   \n",
       "4  莱昂纳多·迪卡普里奥/凯特·温丝莱特/比利·赞恩/凯西·贝茨/弗兰西丝·费舍/格劳瑞亚·斯图...  剧情/爱情/灾难         美国   \n",
       "\n",
       "                                                上映日期                     片长  \\\n",
       "0                  1994-09-10(多伦多电影节)/1994-10-14(美国)                  142分钟   \n",
       "1                  1993-01-01(中国香港)/1993-07-26(中国大陆)     171分钟/155分钟(美国剧场版)   \n",
       "2                   1994-06-23(洛杉矶首映)/1994-07-06(美国)                  142分钟   \n",
       "3                                     1994-09-14(法国)  110分钟(剧场版)/133分钟(国际版)   \n",
       "4  1998-04-03(中国大陆)/1997-11-01(东京电影节)/1997-12-19(美国)       194分钟/227分钟(白星版)   \n",
       "\n",
       "    评分     评分人数  豆瓣排名  \n",
       "0  9.7  2100927     1  \n",
       "1  9.6  1557983     2  \n",
       "2  9.5  1587792     3  \n",
       "3  9.4  1775679     4  \n",
       "4  9.4  1540577     5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 选取需要的几列进行分析\n",
    "columns_remain = ['电影名称', '导演', '主演', '类型',\n",
    "                  '制片国家/地区', '上映日期', '片长', '评分', '评分人数']\n",
    "df = df[columns_remain]\n",
    "\n",
    "# 增加一列豆瓣排名\n",
    "df['豆瓣排名'] = list(np.arange(1, 251))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 250 entries, 0 to 249\n",
      "Data columns (total 10 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   电影名称     250 non-null    object \n",
      " 1   导演       250 non-null    object \n",
      " 2   主演       249 non-null    object \n",
      " 3   类型       250 non-null    object \n",
      " 4   制片国家/地区  250 non-null    object \n",
      " 5   上映日期     250 non-null    object \n",
      " 6   片长       250 non-null    object \n",
      " 7   评分       250 non-null    float64\n",
      " 8   评分人数     250 non-null    int64  \n",
      " 9   豆瓣排名     250 non-null    int64  \n",
      "dtypes: float64(1), int64(2), object(7)\n",
      "memory usage: 19.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# 查看数据的基本信息\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "电影名称       False\n",
       "导演         False\n",
       "主演          True\n",
       "类型         False\n",
       "制片国家/地区    False\n",
       "上映日期       False\n",
       "片长         False\n",
       "评分         False\n",
       "评分人数       False\n",
       "豆瓣排名       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 判断哪些列有空值\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "主演这一列有空值，数据有249条，说明存在1个空值，详细看一下是哪一部电影；  \n",
    "评分列格式是浮点型，评分人数是整型，都没有空值，后面可以直接进行分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>电影名称</th>\n",
       "      <th>导演</th>\n",
       "      <th>主演</th>\n",
       "      <th>类型</th>\n",
       "      <th>制片国家/地区</th>\n",
       "      <th>上映日期</th>\n",
       "      <th>片长</th>\n",
       "      <th>评分</th>\n",
       "      <th>评分人数</th>\n",
       "      <th>豆瓣排名</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>二十二</td>\n",
       "      <td>郭柯</td>\n",
       "      <td>NaN</td>\n",
       "      <td>纪录片</td>\n",
       "      <td>中国大陆</td>\n",
       "      <td>2017-08-14(中国大陆)/2015-10-05(釜山电影节)/2016-06-25(...</td>\n",
       "      <td>99分钟(公映版)/112分钟</td>\n",
       "      <td>8.7</td>\n",
       "      <td>218835</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    电影名称  导演   主演   类型 制片国家/地区  \\\n",
       "188  二十二  郭柯  NaN  纪录片    中国大陆   \n",
       "\n",
       "                                                  上映日期               片长   评分  \\\n",
       "188  2017-08-14(中国大陆)/2015-10-05(釜山电影节)/2016-06-25(...  99分钟(公映版)/112分钟  8.7   \n",
       "\n",
       "       评分人数  豆瓣排名  \n",
       "188  218835   189  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 筛选出主演为空值的数据\n",
    "df[df['主演'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "纪录片《二十二》的主演有空值，这个空值填充“缺失数据”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "电影名称       False\n",
       "导演         False\n",
       "主演         False\n",
       "类型         False\n",
       "制片国家/地区    False\n",
       "上映日期       False\n",
       "片长         False\n",
       "评分         False\n",
       "评分人数       False\n",
       "豆瓣排名       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['主演'].fillna('缺失数据',inplace=True)\n",
    "df.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 判断是否有重复值\n",
    "df['电影名称'].duplicated().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最重要的电影这一列没有重复值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>评分</th>\n",
       "      <th>评分人数</th>\n",
       "      <th>豆瓣排名</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>250.000000</td>\n",
       "      <td>2.500000e+02</td>\n",
       "      <td>250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.888000</td>\n",
       "      <td>5.267182e+05</td>\n",
       "      <td>125.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.263769</td>\n",
       "      <td>3.352101e+05</td>\n",
       "      <td>72.312977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.400000</td>\n",
       "      <td>9.272100e+04</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.700000</td>\n",
       "      <td>3.003325e+05</td>\n",
       "      <td>63.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.800000</td>\n",
       "      <td>4.387350e+05</td>\n",
       "      <td>125.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.100000</td>\n",
       "      <td>6.442718e+05</td>\n",
       "      <td>187.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.700000</td>\n",
       "      <td>2.100927e+06</td>\n",
       "      <td>250.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               评分          评分人数        豆瓣排名\n",
       "count  250.000000  2.500000e+02  250.000000\n",
       "mean     8.888000  5.267182e+05  125.500000\n",
       "std      0.263769  3.352101e+05   72.312977\n",
       "min      8.400000  9.272100e+04    1.000000\n",
       "25%      8.700000  3.003325e+05   63.250000\n",
       "50%      8.800000  4.387350e+05  125.500000\n",
       "75%      9.100000  6.442718e+05  187.750000\n",
       "max      9.700000  2.100927e+06  250.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看离散型变量的情况\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "评分情况，平均值8.888，最高评分9.7，后面进行可视化处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "豆瓣top250榜单详细电影名称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['肖申克的救赎', '霸王别姬', '阿甘正传', '这个杀手不太冷', '泰坦尼克号', '美丽人生', '千与千寻', '辛德勒的名单', '盗梦空间', '忠犬八公的故事', '海上钢琴师', '楚门的世界', '三傻大闹宝莱坞', '机器人总动员', '放牛班的春天', '星际穿越', '大话西游之大圣娶亲', '熔炉', '疯狂动物城', '无间道', '龙猫', '教父', '当幸福来敲门', '怦然心动', '触不可及', '控方证人', '蝙蝠侠：黑暗骑士', '活着', '寻梦环游记', '末代皇帝', '乱世佳人', '何以为家', '摔跤吧！爸爸', '指环王3：王者无敌', '飞屋环游记', '少年派的奇幻漂流', '十二怒汉', '鬼子来了', '素媛', '哈尔的移动城堡', '天空之城', '大话西游之月光宝盒', '哈利·波特与魔法石', '天堂电影院', '罗马假日', '闻香识女人', '辩护人', '我不是药神', '搏击俱乐部', '教父2', '死亡诗社', '狮子王', '指环王2：双塔奇兵', '大闹天宫', '猫鼠游戏', '窃听风暴', '指环王1：魔戒再现', '钢琴家', '黑客帝国', '美丽心灵', '两杆大烟枪', '饮食男女', '飞越疯人院', '本杰明·巴顿奇事', '让子弹飞', 'V字仇杀队', '看不见的客人', '绿皮书', '西西里的美丽传说', '小鞋子', '拯救大兵瑞恩', '海蒂和爷爷', '海豚湾', '穿条纹睡衣的男孩', '情书', '音乐之声', '美国往事', '致命魔术', '低俗小说', '七宗罪', '沉默的羔羊', '蝴蝶效应', '禁闭岛', '心灵捕手', '春光乍泄', '布达佩斯大饭店', '被嫌弃的松子的一生', '阿凡达', '摩登时代', '剪刀手爱德华', '勇敢的心', '喜剧之王', '致命ID', '天使爱美丽', '加勒比海盗', '哈利·波特与死亡圣器(下)', '断背山', '杀人回忆', '狩猎', '请以你的名字呼唤我', '幽灵公主', '阳光灿烂的日子', '小森林', '7号房的礼物', '重庆森林', '第六感', '入殓师', '红辣椒', '小森林', '消失的爱人', '爱在黎明破晓前', '一一', '侧耳倾听', '唐伯虎点秋香', '超脱', '倩女幽魂', '蝙蝠侠：黑暗骑士崛起', '玛丽和马克思', '告白', '无人知晓', '甜蜜蜜', '萤火之森', '大鱼', '阳光姐妹淘', '超能陆战队', '驯龙高手', '射雕英雄传之东成西就', '借东西的小人阿莉埃蒂', '幸福终点站', '菊次郎的夏天', '爱在日落黄昏时', '恐怖直播', '完美的世界', '功夫', '人生果实', '神偷奶爸', '怪兽电力公司', '玩具总动员3', '傲慢与偏见', '血战钢锯岭', '天书奇谭', '风之谷', '教父3', '时空恋旅人', '上帝之城', '电锯惊魂', '釜山行', '被解救的姜戈', '哪吒闹海', '喜宴', '七武士', '英雄本色', '谍影重重3', '我是山姆', '哈利·波特与阿兹卡班的囚徒', '岁月神偷', '头号玩家', '纵横四海', '疯狂原始人', '三块广告牌', '心迷宫', '一个叫欧维的男人决定去死', '达拉斯买家俱乐部', '卢旺达饭店', '萤火虫之墓', '荒蛮故事', '你的名字。', '花样年华', '贫民窟的百万富翁', '真爱至上', '记忆碎片', '东邪西毒', '爆裂鼓手', '哈利·波特与密室', '忠犬八公物语', '黑客帝国3：矩阵革命', '黑天鹅', '模仿游戏', '头脑特工队', '新世界', '未麻的部屋', '你看起来好像很好吃', '雨人', '海街日记', '无敌破坏王', '冰川时代', '恋恋笔记本', '惊魂记', '二十二', '海边的曼彻斯特', '奇迹男孩', '恐怖游轮', '房间', '虎口脱险', '魔女宅急便', '雨中曲', '小偷家族', '绿里奇迹', '疯狂的石头', '人工智能', '爱在午夜降临前', '罗生门', '终结者2：审判日', '初恋这件小事', '海洋', '魂断蓝桥', '可可西里', '2001太空漫游', '燃情岁月', '城市之光', '穿越时空的少女', '牯岭街少年杀人事件', '色，戒', '新龙门客栈', '青蛇', '无耻混蛋', '源代码', '完美陌生人', '阿飞正传', '遗愿清单', '血钻', '大佛普拉斯', '谍影重重2', '香水', '地球上的星星', '九品芝麻官', '战争之王', '疯狂的麦克斯4：狂暴之路', '谍影重重', '步履不停', '彗星来的那一夜', '哈利·波特与火焰杯', '猜火车', '朗读者', '浪潮', '小萝莉的猴神大叔', '驴得水', '波西米亚狂想曲', '再次出发之纽约遇见你', '东京物语', '聚焦', '追随', '千钧一发', '我爱你', '一次别离', '黑鹰坠落', '网络谜踪', '四个春天', '黑客帝国2：重装上阵', '发条橙']\n"
     ]
    }
   ],
   "source": [
    "# 获取榜单里电影名称的中文名称\n",
    "df['电影名称'] = df['电影名称'].apply(lambda x: x.split(' ')[0])\n",
    "print(df['电影名称'].values.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "很多单元格数据是以“/”来分割的，需要转化为列表形式，便于进行分析；  \n",
    "上映日期只要具体年份（年），片长只要具体时长（分钟）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义函数，将用\"/\" 分割的数据转化为列表\n",
    "def conver_list(df, *columns):\n",
    "    for column in columns:\n",
    "        df[column] = df[column].str.split('/')\n",
    "    return df\n",
    "\n",
    "\n",
    "# 定义函数，用来获取上映日期\n",
    "def get_date(x):\n",
    "    find = re.compile('\\d{4}')\n",
    "    date = find.findall(x)[0]\n",
    "    return date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>电影名称</th>\n",
       "      <th>导演</th>\n",
       "      <th>主演</th>\n",
       "      <th>类型</th>\n",
       "      <th>制片国家/地区</th>\n",
       "      <th>上映日期</th>\n",
       "      <th>片长</th>\n",
       "      <th>评分</th>\n",
       "      <th>评分人数</th>\n",
       "      <th>豆瓣排名</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>肖申克的救赎</td>\n",
       "      <td>[弗兰克·德拉邦特]</td>\n",
       "      <td>[蒂姆·罗宾斯, 摩根·弗里曼, 鲍勃·冈顿, 威廉姆·赛德勒, 克兰西·布朗, 吉尔·贝罗...</td>\n",
       "      <td>[剧情, 犯罪]</td>\n",
       "      <td>[美国]</td>\n",
       "      <td>1994</td>\n",
       "      <td>142</td>\n",
       "      <td>9.7</td>\n",
       "      <td>2100927</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>霸王别姬</td>\n",
       "      <td>[陈凯歌]</td>\n",
       "      <td>[张国荣, 张丰毅, 巩俐, 葛优, 英达, 蒋雯丽, 吴大维, 吕齐, 雷汉, 尹治, 马...</td>\n",
       "      <td>[剧情, 爱情, 同性]</td>\n",
       "      <td>[中国大陆, 中国香港]</td>\n",
       "      <td>1993</td>\n",
       "      <td>171</td>\n",
       "      <td>9.6</td>\n",
       "      <td>1557983</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>阿甘正传</td>\n",
       "      <td>[罗伯特·泽米吉斯]</td>\n",
       "      <td>[汤姆·汉克斯, 罗宾·怀特, 加里·西尼斯, 麦凯尔泰·威廉逊, 莎莉·菲尔德, 海利·乔...</td>\n",
       "      <td>[剧情, 爱情]</td>\n",
       "      <td>[美国]</td>\n",
       "      <td>1994</td>\n",
       "      <td>142</td>\n",
       "      <td>9.5</td>\n",
       "      <td>1587792</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>这个杀手不太冷</td>\n",
       "      <td>[吕克·贝松]</td>\n",
       "      <td>[让·雷诺, 娜塔莉·波特曼, 加里·奥德曼, 丹尼·爱罗, 彼得·阿佩尔, 迈克尔·巴达鲁...</td>\n",
       "      <td>[剧情, 动作, 犯罪]</td>\n",
       "      <td>[法国, 美国]</td>\n",
       "      <td>1994</td>\n",
       "      <td>110</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1775679</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>泰坦尼克号</td>\n",
       "      <td>[詹姆斯·卡梅隆]</td>\n",
       "      <td>[莱昂纳多·迪卡普里奥, 凯特·温丝莱特, 比利·赞恩, 凯西·贝茨, 弗兰西丝·费舍, 格...</td>\n",
       "      <td>[剧情, 爱情, 灾难]</td>\n",
       "      <td>[美国]</td>\n",
       "      <td>1998</td>\n",
       "      <td>194</td>\n",
       "      <td>9.4</td>\n",
       "      <td>1540577</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      电影名称          导演                                                 主演  \\\n",
       "0   肖申克的救赎  [弗兰克·德拉邦特]  [蒂姆·罗宾斯, 摩根·弗里曼, 鲍勃·冈顿, 威廉姆·赛德勒, 克兰西·布朗, 吉尔·贝罗...   \n",
       "1     霸王别姬       [陈凯歌]  [张国荣, 张丰毅, 巩俐, 葛优, 英达, 蒋雯丽, 吴大维, 吕齐, 雷汉, 尹治, 马...   \n",
       "2     阿甘正传  [罗伯特·泽米吉斯]  [汤姆·汉克斯, 罗宾·怀特, 加里·西尼斯, 麦凯尔泰·威廉逊, 莎莉·菲尔德, 海利·乔...   \n",
       "3  这个杀手不太冷     [吕克·贝松]  [让·雷诺, 娜塔莉·波特曼, 加里·奥德曼, 丹尼·爱罗, 彼得·阿佩尔, 迈克尔·巴达鲁...   \n",
       "4    泰坦尼克号   [詹姆斯·卡梅隆]  [莱昂纳多·迪卡普里奥, 凯特·温丝莱特, 比利·赞恩, 凯西·贝茨, 弗兰西丝·费舍, 格...   \n",
       "\n",
       "             类型       制片国家/地区  上映日期   片长   评分     评分人数  豆瓣排名  \n",
       "0      [剧情, 犯罪]          [美国]  1994  142  9.7  2100927     1  \n",
       "1  [剧情, 爱情, 同性]  [中国大陆, 中国香港]  1993  171  9.6  1557983     2  \n",
       "2      [剧情, 爱情]          [美国]  1994  142  9.5  1587792     3  \n",
       "3  [剧情, 动作, 犯罪]      [法国, 美国]  1994  110  9.4  1775679     4  \n",
       "4  [剧情, 爱情, 灾难]          [美国]  1998  194  9.4  1540577     5  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# '上映日期'和'片长'这两列，取具体的数字\n",
    "df['上映日期'] = df['上映日期'].apply(get_date)\n",
    "df['片长'] = df['片长'].map(lambda x: x.split('分钟')[0])\n",
    "\n",
    "#  '导演', '主演', '类型', '制片国家/地区'，这几列下面的值转化为列表\n",
    "df = conver_list(df, '导演', '主演', '类型', '制片国家/地区')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析及可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 电影上映日期分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'2010': 13, '2004': 13, '2013': 11, '2015': 11, '1994': 10, '2011': 10, '2003': 10, '2008': 9, '2018': 9, '2017': 9, '1995': 8, '2006': 8, '1993': 7, '2019': 7, '2009': 7, '2002': 7, '1999': 7, '2014': 6, '2012': 6, '2001': 6, '2005': 6, '1997': 6, '1998': 5, '2016': 5, '2000': 4, '1992': 4, '1991': 4, '2007': 4, '1987': 3, '1988': 3, '1990': 3, '1957': 2, '1953': 2, '1989': 2, '1996': 2, '2020': 1, '1972': 1, '1939': 1, '1974': 1, '1961': 1, '1975': 1, '1965': 1, '1984': 1, '1936': 1, '1983': 1, '1979': 1, '1954': 1, '1986': 1, '1960': 1, '1966': 1, '1952': 1, '1950': 1, '1940': 1, '1968': 1, '1931': 1, '1971': 1})\n"
     ]
    }
   ],
   "source": [
    "# 获取每一年上映的电影数量\n",
    "list_date = df['上映日期'].tolist()\n",
    "date_count = collections.Counter(list_date)\n",
    "print(date_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('1931', 1), ('1936', 1), ('1939', 1), ('1940', 1), ('1950', 1)]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按照电影上映年份进行排序\n",
    "sort_date_count = sorted(date_count.items(), key=lambda x: x[0])\n",
    "sort_date_count[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "    <div id=\"e404c35b06dc4a9d89ea64bc78f88262\" style=\"width:990px;height:500px;\"></div>\n",
       "\n",
       "\n",
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       "            \"saveAsImage\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"save as image\"\n",
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       "                \"show\": true,\n",
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       "            \"barCategoryGap\": \"20%\",\n",
       "            \"label\": {\n",
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       "        }\n",
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       "    \"legend\": [\n",
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       "            \"textStyle\": {\n",
       "                \"fontSize\": 12\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"animation\": true,\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"nameLocation\": \"middle\",\n",
       "            \"nameGap\": 25,\n",
       "            \"nameTextStyle\": {\n",
       "                \"fontSize\": 14\n",
       "            },\n",
       "            \"axisTick\": {\n",
       "                \"alignWithLabel\": false\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"boundaryGap\": true,\n",
       "            \"type\": \"category\",\n",
       "            \"splitLine\": {\n",
       "                \"show\": false\n",
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       "            \"axisLine\": {\n",
       "                \"lineStyle\": {\n",
       "                    \"width\": 1\n",
       "                }\n",
       "            },\n",
       "            \"axisLabel\": {\n",
       "                \"interval\": 0,\n",
       "                \"rotate\": 60,\n",
       "                \"margin\": 8,\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 12\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"1931\",\n",
       "                \"1936\",\n",
       "                \"1939\",\n",
       "                \"1940\",\n",
       "                \"1950\",\n",
       "                \"1952\",\n",
       "                \"1953\",\n",
       "                \"1954\",\n",
       "                \"1957\",\n",
       "                \"1960\",\n",
       "                \"1961\",\n",
       "                \"1965\",\n",
       "                \"1966\",\n",
       "                \"1968\",\n",
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       "                \"1974\",\n",
       "                \"1975\",\n",
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       "                \"1984\",\n",
       "                \"1986\",\n",
       "                \"1987\",\n",
       "                \"1988\",\n",
       "                \"1989\",\n",
       "                \"1990\",\n",
       "                \"1991\",\n",
       "                \"1992\",\n",
       "                \"1993\",\n",
       "                \"1994\",\n",
       "                \"1995\",\n",
       "                \"1996\",\n",
       "                \"1997\",\n",
       "                \"1998\",\n",
       "                \"1999\",\n",
       "                \"2000\",\n",
       "                \"2001\",\n",
       "                \"2002\",\n",
       "                \"2003\",\n",
       "                \"2004\",\n",
       "                \"2005\",\n",
       "                \"2006\",\n",
       "                \"2007\",\n",
       "                \"2008\",\n",
       "                \"2009\",\n",
       "                \"2010\",\n",
       "                \"2011\",\n",
       "                \"2012\",\n",
       "                \"2013\",\n",
       "                \"2014\",\n",
       "                \"2015\",\n",
       "                \"2016\",\n",
       "                \"2017\",\n",
       "                \"2018\",\n",
       "                \"2019\",\n",
       "                \"2020\"\n",
       "            ]\n",
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       "    \"yAxis\": [\n",
       "        {\n",
       "            \"name\": \"\\u7535\\u5f71\\u6570\\u91cf\",\n",
       "            \"show\": true,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 20,\n",
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       "            \"boundaryGap\": true,\n",
       "            \"type\": \"value\",\n",
       "            \"splitLine\": {\n",
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       "                \"formatter\": \"{value} \",\n",
       "                \"rotate\": 0,\n",
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       "    \"color\": [\n",
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       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
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       "        \"#749f83\",\n",
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       "        \"#f47920\",\n",
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       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\",\n",
       "        \"#f6f5ec\"\n",
       "    ],\n",
       "    \"dataZoom\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"type\": \"slider\",\n",
       "            \"start\": 50,\n",
       "            \"end\": 100,\n",
       "            \"orient\": \"horizontal\"\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "myChart_e404c35b06dc4a9d89ea64bc78f88262.setOption(option_e404c35b06dc4a9d89ea64bc78f88262);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.bar.Bar at 0x2768d6c91c8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将排序后的字典转化为新的列表，作为图表横坐标轴、纵坐标轴\n",
    "x1 = [x[0] for x in sort_date_count]\n",
    "y1 = [x[1] for x in sort_date_count]\n",
    "\n",
    "\n",
    "# 用pyecharts绘制柱状图\n",
    "bar = pe.Bar('豆瓣top250电影上映年份分布情况', height=500, width=990, title_pos='center')\n",
    "\n",
    "# is_label_show=True,显示数据标签;is_datazoom_show=True控制条，xaxis_interval=0,所有横坐标标签都显示,xaxis_rotate=60,标签旋转60度\n",
    "bar.add('', x1, y1, is_label_show=True, is_datazoom_show=True, xaxis_interval=0,\n",
    "        xaxis_rotate=60, yaxis_name='电影数量', yaxis_name_gap=20, yaxis_name_pos='end')\n",
    "bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "豆瓣top250电影中，最早的一部是在1931年，名称是卓别林主演的《城市之光》，最新一部是在2020年在中国上映的《美丽人生》，大部分电影上映年份分布在1987年到2019年之间，其中，2004年和2010年最多，都是13部；另外认真看图表可以发现，1993年和1994年，好电影数量比1992年明显上升，2003年和2004年也有类似情况发生。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>电影名称</th>\n",
       "      <th>导演</th>\n",
       "      <th>主演</th>\n",
       "      <th>类型</th>\n",
       "      <th>制片国家/地区</th>\n",
       "      <th>上映日期</th>\n",
       "      <th>片长</th>\n",
       "      <th>评分</th>\n",
       "      <th>评分人数</th>\n",
       "      <th>豆瓣排名</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>城市之光</td>\n",
       "      <td>[查理·卓别林]</td>\n",
       "      <td>[查理·卓别林, 弗吉尼亚·切瑞尔, 佛罗伦斯·李, 亨利·伯格曼, 珍·哈露]</td>\n",
       "      <td>[剧情, 喜剧, 爱情]</td>\n",
       "      <td>[美国]</td>\n",
       "      <td>1931</td>\n",
       "      <td>87</td>\n",
       "      <td>9.3</td>\n",
       "      <td>92721</td>\n",
       "      <td>210</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     电影名称        导演                                        主演            类型  \\\n",
       "209  城市之光  [查理·卓别林]  [查理·卓别林, 弗吉尼亚·切瑞尔, 佛罗伦斯·李, 亨利·伯格曼, 珍·哈露]  [剧情, 喜剧, 爱情]   \n",
       "\n",
       "    制片国家/地区  上映日期  片长   评分   评分人数  豆瓣排名  \n",
       "209    [美国]  1931  87  9.3  92721   210  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['上映日期']=='1931']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>电影名称</th>\n",
       "      <th>导演</th>\n",
       "      <th>主演</th>\n",
       "      <th>类型</th>\n",
       "      <th>制片国家/地区</th>\n",
       "      <th>上映日期</th>\n",
       "      <th>片长</th>\n",
       "      <th>评分</th>\n",
       "      <th>评分人数</th>\n",
       "      <th>豆瓣排名</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>美丽人生</td>\n",
       "      <td>[罗伯托·贝尼尼]</td>\n",
       "      <td>[罗伯托·贝尼尼, 尼可莱塔·布拉斯基, 乔治·坎塔里尼, 朱斯蒂诺·杜拉诺, 赛尔乔·比尼...</td>\n",
       "      <td>[剧情, 喜剧, 爱情, 战争]</td>\n",
       "      <td>[意大利]</td>\n",
       "      <td>2020</td>\n",
       "      <td>116</td>\n",
       "      <td>9.5</td>\n",
       "      <td>991942</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   电影名称         导演                                                 主演  \\\n",
       "5  美丽人生  [罗伯托·贝尼尼]  [罗伯托·贝尼尼, 尼可莱塔·布拉斯基, 乔治·坎塔里尼, 朱斯蒂诺·杜拉诺, 赛尔乔·比尼...   \n",
       "\n",
       "                 类型 制片国家/地区  上映日期   片长   评分    评分人数  豆瓣排名  \n",
       "5  [剧情, 喜剧, 爱情, 战争]   [意大利]  2020  116  9.5  991942     6  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['上映日期']=='2020']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 电影制片国家地区分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['美国', '中国大陆', '中国香港', '美国', '法国', '美国', '美国', '意大利', '日本', '美国']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_list = []\n",
    "for i in df['制片国家/地区']:\n",
    "    area_list.extend(i)\n",
    "area_list[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "373"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(area_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "制片国家/地区的总数量超过250,是因为部分电影是多个国家或地区合作的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'美国': 137, '英国': 35, '日本': 34, '中国香港': 28, '中国大陆': 23, '法国': 20, '德国': 18, '韩国': 11, '意大利': 9, '中国台湾': 8, '加拿大': 7, '澳大利亚': 7, '印度': 4, '瑞士': 4, '西班牙': 4, '新西兰': 3, '伊朗': 2, '南非': 2, '瑞典': 2, '巴西': 2, '冰岛': 1, '黎巴嫩': 1, '波兰': 1, '丹麦': 1, '荷兰': 1, '奥地利': 1, '阿根廷': 1, '爱尔兰': 1, '希腊': 1, '泰国': 1, '阿联酋': 1, '捷克': 1, '俄罗斯': 1})\n"
     ]
    }
   ],
   "source": [
    "area_count = collections.Counter(area_list)\n",
    "print(area_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('美国', 137), ('英国', 35), ('日本', 34), ('中国香港', 28), ('中国大陆', 23)]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort_area_count = sorted(area_count.items(), key=lambda x: x[1], reverse=True)\n",
    "sort_area_count[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "    require(['echarts'], function(echarts) {\n",
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       "var myChart_142484ad3e444c25a0cb770bf6bbbff6 = echarts.init(document.getElementById('142484ad3e444c25a0cb770bf6bbbff6'), 'light', {renderer: 'canvas'});\n",
       "\n",
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       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u8c46\\u74e3top250\\u7535\\u5f71\\u5236\\u7247\\u56fd\\u5bb6/\\u5730\\u533a\\u5206\\u5e03\\u60c5\\u51b5\",\n",
       "            \"subtext\": \"\\u90e8\\u5206\\u7535\\u5f71\\u662f\\u591a\\u4e2a\\u56fd\\u5bb6\\u6216\\u5730\\u533a\\u5408\\u4f5c\",\n",
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       "                \"fontSize\": 18\n",
       "            },\n",
       "            \"subtextStyle\": {\n",
       "                \"fontSize\": 12\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"toolbox\": {\n",
       "        \"show\": true,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"left\": \"95%\",\n",
       "        \"top\": \"center\",\n",
       "        \"feature\": {\n",
       "            \"saveAsImage\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"save as image\"\n",
       "            },\n",
       "            \"restore\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"restore\"\n",
       "            },\n",
       "            \"dataView\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"data view\"\n",
       "            }\n",
       "        }\n",
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       "        \"trigger\": \"item\",\n",
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       "        \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n",
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       "            \"barCategoryGap\": \"20%\",\n",
       "            \"label\": {\n",
       "                \"normal\": {\n",
       "                    \"show\": true,\n",
       "                    \"position\": \"top\",\n",
       "                    \"textStyle\": {\n",
       "                        \"fontSize\": 12\n",
       "                    }\n",
       "                },\n",
       "                \"emphasis\": {\n",
       "                    \"show\": true,\n",
       "                    \"textStyle\": {\n",
       "                        \"fontSize\": 12\n",
       "                    }\n",
       "                }\n",
       "            },\n",
       "            \"markPoint\": {\n",
       "                \"data\": []\n",
       "            },\n",
       "            \"markLine\": {\n",
       "                \"data\": []\n",
       "            },\n",
       "            \"seriesId\": 422269\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\"\n",
       "            ],\n",
       "            \"selectedMode\": \"multiple\",\n",
       "            \"show\": true,\n",
       "            \"left\": \"center\",\n",
       "            \"top\": \"top\",\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"textStyle\": {\n",
       "                \"fontSize\": 12\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"animation\": true,\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"nameLocation\": \"middle\",\n",
       "            \"nameGap\": 25,\n",
       "            \"nameTextStyle\": {\n",
       "                \"fontSize\": 14\n",
       "            },\n",
       "            \"axisTick\": {\n",
       "                \"alignWithLabel\": false\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"boundaryGap\": true,\n",
       "            \"type\": \"category\",\n",
       "            \"splitLine\": {\n",
       "                \"show\": false\n",
       "            },\n",
       "            \"axisLine\": {\n",
       "                \"lineStyle\": {\n",
       "                    \"width\": 1\n",
       "                }\n",
       "            },\n",
       "            \"axisLabel\": {\n",
       "                \"interval\": 0,\n",
       "                \"rotate\": 45,\n",
       "                \"margin\": 8,\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 12\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"\\u7f8e\\u56fd\",\n",
       "                \"\\u82f1\\u56fd\",\n",
       "                \"\\u65e5\\u672c\",\n",
       "                \"\\u4e2d\\u56fd\\u9999\\u6e2f\",\n",
       "                \"\\u4e2d\\u56fd\\u5927\\u9646\",\n",
       "                \"\\u6cd5\\u56fd\",\n",
       "                \"\\u5fb7\\u56fd\",\n",
       "                \"\\u97e9\\u56fd\",\n",
       "                \"\\u610f\\u5927\\u5229\",\n",
       "                \"\\u4e2d\\u56fd\\u53f0\\u6e7e\",\n",
       "                \"\\u52a0\\u62ff\\u5927\",\n",
       "                \"\\u6fb3\\u5927\\u5229\\u4e9a\",\n",
       "                \"\\u5370\\u5ea6\",\n",
       "                \"\\u745e\\u58eb\",\n",
       "                \"\\u897f\\u73ed\\u7259\",\n",
       "                \"\\u65b0\\u897f\\u5170\",\n",
       "                \"\\u4f0a\\u6717\",\n",
       "                \"\\u5357\\u975e\",\n",
       "                \"\\u745e\\u5178\",\n",
       "                \"\\u5df4\\u897f\",\n",
       "                \"\\u51b0\\u5c9b\",\n",
       "                \"\\u9ece\\u5df4\\u5ae9\",\n",
       "                \"\\u6ce2\\u5170\",\n",
       "                \"\\u4e39\\u9ea6\",\n",
       "                \"\\u8377\\u5170\",\n",
       "                \"\\u5965\\u5730\\u5229\",\n",
       "                \"\\u963f\\u6839\\u5ef7\",\n",
       "                \"\\u7231\\u5c14\\u5170\",\n",
       "                \"\\u5e0c\\u814a\",\n",
       "                \"\\u6cf0\\u56fd\",\n",
       "                \"\\u963f\\u8054\\u914b\",\n",
       "                \"\\u6377\\u514b\",\n",
       "                \"\\u4fc4\\u7f57\\u65af\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"name\": \"\\u7535\\u5f71\\u6570\\u91cf\",\n",
       "            \"show\": true,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 20,\n",
       "            \"nameTextStyle\": {\n",
       "                \"fontSize\": 14\n",
       "            },\n",
       "            \"axisTick\": {\n",
       "                \"alignWithLabel\": false\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"boundaryGap\": true,\n",
       "            \"type\": \"value\",\n",
       "            \"splitLine\": {\n",
       "                \"show\": true\n",
       "            },\n",
       "            \"axisLine\": {\n",
       "                \"lineStyle\": {\n",
       "                    \"width\": 1\n",
       "                }\n",
       "            },\n",
       "            \"axisLabel\": {\n",
       "                \"interval\": \"auto\",\n",
       "                \"formatter\": \"{value} \",\n",
       "                \"rotate\": 0,\n",
       "                \"margin\": 8,\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 12\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\",\n",
       "        \"#f6f5ec\"\n",
       "    ]\n",
       "};\n",
       "myChart_142484ad3e444c25a0cb770bf6bbbff6.setOption(option_142484ad3e444c25a0cb770bf6bbbff6);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.bar.Bar at 0x2768d703388>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将排序后的字典转化为新的列表，作为横坐标轴、纵坐标轴\n",
    "x2 = [x[0] for x in sort_area_count]\n",
    "y2 = [x[1] for x in sort_area_count]\n",
    "\n",
    "\n",
    "# 用pyecharts绘制柱状图\n",
    "bar = pe.Bar('豆瓣top250电影制片国家/地区分布情况', '部分电影是多个国家或地区合作',\n",
    "             height=500, width=980, title_pos='center')\n",
    "\n",
    "\n",
    "bar.add('', x2, y2, is_label_show=True, xaxis_interval=0, xaxis_rotate=45,\n",
    "        yaxis_name='电影数量', yaxis_name_gap=20, yaxis_name_pos='end')\n",
    "bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "豆瓣top250电影，有137部电影是美国出品的，超过一半；接着是英国和日本，中国（含大陆、香港、台湾）出品的有59部。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 电影类型分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "type_list = []\n",
    "for i in df['类型']:\n",
    "    type_list.extend(i)\n",
    "\n",
    "type_count = collections.Counter(type_list)\n",
    "sort_type_count = sorted(type_count.items(), key=lambda x: x[1], reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "250部电影标签总数是：690，平均每部电影标签数量是：2.76。\n",
      "Counter({'剧情': 183, '爱情': 55, '喜剧': 53, '冒险': 44, '犯罪': 43, '奇幻': 40, '动画': 34, '惊悚': 34, '动作': 33, '悬疑': 30, '科幻': 24, '家庭': 23, '战争': 16, '传记': 15, '历史': 10, '古装': 9, '同性': 8, '音乐': 7, '歌舞': 5, '儿童': 5, '纪录片': 5, '武侠': 4, '西部': 3, '灾难': 2, '情色': 2, '恐怖': 2, '运动': 1})\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts': '/nbextensions/echarts/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "    <div id=\"e1bcb20b98cd4e8ba41fc8586ccb3f74\" style=\"width:900px;height:750px;\"></div>\n",
       "\n",
       "\n",
       "<script>\n",
       "    require(['echarts'], function(echarts) {\n",
       "        \n",
       "var myChart_e1bcb20b98cd4e8ba41fc8586ccb3f74 = echarts.init(document.getElementById('e1bcb20b98cd4e8ba41fc8586ccb3f74'), 'light', {renderer: 'canvas'});\n",
       "\n",
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       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u8c46\\u74e3top250\\u7535\\u5f71\\u7c7b\\u578b\\u6807\\u7b7e\\u5206\\u5e03\\u60c5\\u51b5\",\n",
       "            \"subtext\": \"\\u90e8\\u5206\\u7535\\u5f71\\u6709\\u591a\\u4e2a\\u6807\\u7b7e\",\n",
       "            \"left\": \"center\",\n",
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       "            \"textStyle\": {\n",
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       "            },\n",
       "            \"subtextStyle\": {\n",
       "                \"fontSize\": 12\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"toolbox\": {\n",
       "        \"show\": true,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"left\": \"95%\",\n",
       "        \"top\": \"center\",\n",
       "        \"feature\": {\n",
       "            \"saveAsImage\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"save as image\"\n",
       "            },\n",
       "            \"restore\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"restore\"\n",
       "            },\n",
       "            \"dataView\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"data view\"\n",
       "            }\n",
       "        }\n",
       "    },\n",
       "    \"series_id\": 8594252,\n",
       "    \"tooltip\": {\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
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       "        \"borderColor\": \"#333\",\n",
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       "                    \"value\": 183\n",
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       "                {\n",
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       "                },\n",
       "                {\n",
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       "                    \"value\": 30\n",
       "                },\n",
       "                {\n",
       "                    \"name\": \"\\u79d1\\u5e7b\",\n",
       "                    \"value\": 24\n",
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       "                {\n",
       "                    \"name\": \"\\u5bb6\\u5ead\",\n",
       "                    \"value\": 23\n",
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       "                {\n",
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       "            \"center\": [\n",
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       "                    \"formatter\": \"{b}: {d}%\"\n",
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       "                    \"formatter\": \"{b}: {d}%\"\n",
       "                }\n",
       "            },\n",
       "            \"seriesId\": 8594252\n",
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       "                \"\\u7eaa\\u5f55\\u7247\",\n",
       "                \"\\u6b66\\u4fa0\",\n",
       "                \"\\u897f\\u90e8\",\n",
       "                \"\\u707e\\u96be\",\n",
       "                \"\\u60c5\\u8272\",\n",
       "                \"\\u6050\\u6016\",\n",
       "                \"\\u8fd0\\u52a8\"\n",
       "            ],\n",
       "            \"selectedMode\": \"multiple\",\n",
       "            \"show\": true,\n",
       "            \"left\": \"left\",\n",
       "            \"top\": \"top\",\n",
       "            \"orient\": \"vertical\",\n",
       "            \"textStyle\": {\n",
       "                \"fontSize\": 12\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"animation\": true,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
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       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\",\n",
       "        \"#f6f5ec\"\n",
       "    ]\n",
       "};\n",
       "myChart_e1bcb20b98cd4e8ba41fc8586ccb3f74.setOption(option_e1bcb20b98cd4e8ba41fc8586ccb3f74);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.pie.Pie at 0x2768d719508>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将排序后的字典转化为新的列表，作为横坐标轴、纵坐标轴\n",
    "x3 = [x[0] for x in sort_type_count]\n",
    "y3 = [x[1] for x in sort_type_count]\n",
    "\n",
    "print('250部电影标签总数是：{}，平均每部电影标签数量是：{}。'.format(sum(y3), sum(y3)/250))\n",
    "print(type_count)\n",
    "# 用pyecharts绘制柱状图\n",
    "pie = pe.Pie('豆瓣top250电影类型标签分布情况', '部分电影有多个标签',\n",
    "             height=750, width=900, title_pos='center')\n",
    "\n",
    "# 图例放在图表左边\n",
    "pie.add('', x3, y3, is_label_show=True,\n",
    "        legend_pos='left', legend_orient='vertical')\n",
    "pie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看出，250部电影类型标签众多，有剧情、爱情、犯罪等等，恐怖片也有上榜，是《电锯惊魂》和《惊魂记》。跟制片国家/地区一样，一部电影可能有多个标签，所有类型标签加起来总共有690个，平均每部电影有2.76个标签，26.52%的标签是剧情，并不意味着有26.52%的电影是属于剧情片，实际上，剧情标签数量是183个，意味着250部电影里有183是属于剧情电影，占比达到73.2%，接近3/4。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145    电锯惊魂\n",
       "187     惊魂记\n",
       "Name: 电影名称, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['恐怖' in x for x in df['类型'].tolist()]]['电影名称']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "183"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type_count['剧情']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 电影导演分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<pyecharts.charts.bar.Bar at 0x2768d71d148>"
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     },
     "execution_count": 28,
     "metadata": {},
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   ],
   "source": [
    "director_list = []\n",
    "for x in df['导演']:\n",
    "    director_list.extend(x)\n",
    "director_count = collections.Counter(director_list)\n",
    "sort_director_count = sorted(\n",
    "    director_count.items(), key=lambda x: x[1], reverse=True)\n",
    "\n",
    "x4 = [x[0] for x in sort_director_count]\n",
    "y4 = [x[1] for x in sort_director_count]\n",
    "\n",
    "bar = pe.Bar('豆瓣top250导演数量分布情况(前20位导演)', title_pos='center')\n",
    "bar.add('', x4[:20], y4[:20], is_label_show=True, xaxis_interval=0,\n",
    "        xaxis_rotate=45, yaxis_name='电影数量', yaxis_name_gap=20, yaxis_name_pos='end', yaxis_max=8)\n",
    "bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，宫崎骏和克里斯托弗·诺兰并列第一，都有7部电影入围豆瓣top250，中国导演李安、王家卫分别有4部电影入围豆瓣top250，刘振伟、姜文分别有3部电影入围豆瓣top250，看一下克里斯托弗·诺兰有哪些电影入围豆瓣top250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8            盗梦空间\n",
       "15           星际穿越\n",
       "26       蝙蝠侠：黑暗骑士\n",
       "77           致命魔术\n",
       "116    蝙蝠侠：黑暗骑士崛起\n",
       "170          记忆碎片\n",
       "241            追随\n",
       "Name: 电影名称, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['克里斯托弗·诺兰' in x for x in df['导演'].tolist()]]['电影名称']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 电影主演分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "    ],\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\",\n",
       "        \"#f6f5ec\"\n",
       "    ]\n",
       "};\n",
       "myChart_733982504da64ac3b1ee3058a54eeff6.setOption(option_733982504da64ac3b1ee3058a54eeff6);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.bar.Bar at 0x2768d71da88>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actor_list = []\n",
    "for x in df['主演']:\n",
    "    actor_list.extend(x)\n",
    "\n",
    "actor_count = collections.Counter(actor_list)\n",
    "sort_actor_count = sorted(\n",
    "    actor_count.items(), key=lambda x: x[1], reverse=True)\n",
    "\n",
    "x6 = [x[0] for x in sort_actor_count]\n",
    "y6 = [x[1] for x in sort_actor_count]\n",
    "\n",
    "bar = pe.Bar('豆瓣top250主演数量分布情况(前20位演员)', title_pos='center')\n",
    "bar.add('', x6[:20], y6[:20], is_label_show=True, xaxis_interval=0, xaxis_rotate=45,\n",
    "        yaxis_name='电影数量', yaxis_name_gap=20, yaxis_name_pos='end')\n",
    "bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到很多演员主演的电影质量是很高的，中国演员张国荣、梁朝伟主演的电影，分别有8部入围豆瓣top250，周星驰有6部入围；张国荣、梁朝伟一起主演的电影有4部入围，分别是《春光乍泄》、《射雕英雄传之东成西就》、《东邪西毒》、《阿飞正传》。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1            霸王别姬\n",
       "84           春光乍泄\n",
       "115          倩女幽魂\n",
       "126    射雕英雄传之东成西就\n",
       "151          英雄本色\n",
       "157          纵横四海\n",
       "171          东邪西毒\n",
       "218          阿飞正传\n",
       "Name: 电影名称, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['张国荣' in x for x in df['主演'].tolist()]]['电影名称']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19            无间道\n",
       "84           春光乍泄\n",
       "104          重庆森林\n",
       "126    射雕英雄传之东成西就\n",
       "167          花样年华\n",
       "171          东邪西毒\n",
       "212           色，戒\n",
       "218          阿飞正传\n",
       "Name: 电影名称, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['梁朝伟' in x for x in df['主演'].tolist()]]['电影名称']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84           春光乍泄\n",
       "126    射雕英雄传之东成西就\n",
       "171          东邪西毒\n",
       "218          阿飞正传\n",
       "Name: 电影名称, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[('梁朝伟' in x and '张国荣' in x)for x in df['主演'].tolist()]]['电影名称']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 电影片长分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts': '/nbextensions/echarts/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "    <div id=\"be252982b43c4d2c80bc4dc068b3b458\" style=\"width:800px;height:400px;\"></div>\n",
       "\n",
       "\n",
       "<script>\n",
       "    require(['echarts'], function(echarts) {\n",
       "        \n",
       "var myChart_be252982b43c4d2c80bc4dc068b3b458 = echarts.init(document.getElementById('be252982b43c4d2c80bc4dc068b3b458'), 'light', {renderer: 'canvas'});\n",
       "\n",
       "var option_be252982b43c4d2c80bc4dc068b3b458 = {\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u7535\\u5f71\\u7247\\u957f\\u5206\\u5e03\\u60c5\\u51b5\",\n",
       "            \"left\": \"center\",\n",
       "            \"top\": \"auto\",\n",
       "            \"textStyle\": {\n",
       "                \"fontSize\": 18\n",
       "            },\n",
       "            \"subtextStyle\": {\n",
       "                \"fontSize\": 12\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"toolbox\": {\n",
       "        \"show\": true,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"left\": \"95%\",\n",
       "        \"top\": \"center\",\n",
       "        \"feature\": {\n",
       "            \"saveAsImage\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"save as image\"\n",
       "            },\n",
       "            \"restore\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"restore\"\n",
       "            },\n",
       "            \"dataView\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"data view\"\n",
       "            }\n",
       "        }\n",
       "    },\n",
       "    \"series_id\": 4858670,\n",
       "    \"tooltip\": {\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n",
       "        \"borderColor\": \"#333\",\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"boxplot\",\n",
       "            \"data\": [\n",
       "                [\n",
       "                    45,\n",
       "                    102.0,\n",
       "                    118.0,\n",
       "                    136.25,\n",
       "                    238\n",
       "                ]\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"normal\": {\n",
       "                    \"show\": false,\n",
       "                    \"position\": \"top\",\n",
       "                    \"textStyle\": {\n",
       "                        \"fontSize\": 12\n",
       "                    }\n",
       "                },\n",
       "                \"emphasis\": {\n",
       "                    \"show\": true,\n",
       "                    \"textStyle\": {\n",
       "                        \"fontSize\": 12\n",
       "                    }\n",
       "                }\n",
       "            },\n",
       "            \"markPoint\": {\n",
       "                \"data\": []\n",
       "            },\n",
       "            \"markLine\": {\n",
       "                \"data\": []\n",
       "            },\n",
       "            \"seriesId\": 4858670\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\"\n",
       "            ],\n",
       "            \"selectedMode\": \"multiple\",\n",
       "            \"show\": true,\n",
       "            \"left\": \"center\",\n",
       "            \"top\": \"top\",\n",
       "            \"orient\": \"horizontal\",\n",
       "            \"textStyle\": {\n",
       "                \"fontSize\": 12\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"animation\": true,\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"nameLocation\": \"middle\",\n",
       "            \"nameGap\": 25,\n",
       "            \"nameTextStyle\": {\n",
       "                \"fontSize\": 14\n",
       "            },\n",
       "            \"axisTick\": {\n",
       "                \"alignWithLabel\": false\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"boundaryGap\": true,\n",
       "            \"type\": \"category\",\n",
       "            \"splitLine\": {\n",
       "                \"show\": false\n",
       "            },\n",
       "            \"axisLine\": {\n",
       "                \"lineStyle\": {\n",
       "                    \"width\": 1\n",
       "                }\n",
       "            },\n",
       "            \"axisLabel\": {\n",
       "                \"interval\": \"auto\",\n",
       "                \"rotate\": 0,\n",
       "                \"margin\": 8,\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 12\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"\\u7247\\u957f\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"nameLocation\": \"middle\",\n",
       "            \"nameGap\": 25,\n",
       "            \"nameTextStyle\": {\n",
       "                \"fontSize\": 14\n",
       "            },\n",
       "            \"axisTick\": {\n",
       "                \"alignWithLabel\": false\n",
       "            },\n",
       "            \"inverse\": false,\n",
       "            \"boundaryGap\": true,\n",
       "            \"type\": \"value\",\n",
       "            \"splitLine\": {\n",
       "                \"show\": true\n",
       "            },\n",
       "            \"axisLine\": {\n",
       "                \"lineStyle\": {\n",
       "                    \"width\": 1\n",
       "                }\n",
       "            },\n",
       "            \"axisLabel\": {\n",
       "                \"interval\": \"auto\",\n",
       "                \"formatter\": \"{value} \",\n",
       "                \"rotate\": 0,\n",
       "                \"margin\": 8,\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 12\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\",\n",
       "        \"#f6f5ec\"\n",
       "    ]\n",
       "};\n",
       "myChart_be252982b43c4d2c80bc4dc068b3b458.setOption(option_be252982b43c4d2c80bc4dc068b3b458);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.boxplot.Boxplot at 0x2768d753308>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用箱型图查看电影片长分布情况\n",
    "boxplot = pe.Boxplot('电影片长分布情况', title_pos='center')\n",
    "time_list = sorted(df['片长'].astype('int').tolist())\n",
    "_yaxis = boxplot.prepare_data([time_list])\n",
    "boxplot.add('', ['片长'], _yaxis, islabel_show=True)\n",
    "boxplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "250部电影片长分布挺集中的，其中，最长的电影有238分钟，最短的只有45分钟，平均片长是118分钟，通过查看数据可以发现，片长最长的是《乱世佳人》，片长最短的是《萤火之森》。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30    乱世佳人\n",
      "Name: 电影名称, dtype: object\n",
      "121    萤火之森\n",
      "Name: 电影名称, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df[df['片长']=='238']['电影名称'])\n",
    "print(df[df['片长']=='45']['电影名称'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 电影排名&评分&评分人数情况分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 用matplotlib绘图，将电影排名、评分、评分人数，3个维度的数据绘制在一张图上，更加直观"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x2768de5e048>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x5 = df['评分人数'].tolist()\n",
    "y5 = df['评分'].tolist()\n",
    "z = df['豆瓣排名'].tolist()\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.scatter(x=x5,\n",
    "            y=z,\n",
    "            cmap='Blues',\n",
    "            marker='o',\n",
    "            c=y5,   # 数字越大，颜色越深，评分越高\n",
    "            alpha=0.8,\n",
    "            linewidths=0.3,\n",
    "            edgecolors='Black')\n",
    "\n",
    "plt.title('豆瓣top250电影排名&评分&评分人数情况', fontsize=18)\n",
    "plt.xlabel('评分人数', fontsize=14)\n",
    "plt.ylabel('豆瓣排名', fontsize=14)\n",
    "plt.xlim(0, 2500000)\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从颜色条可以看出，豆瓣top250电影所有评分都在8分以上，最低评分8.4，从散点图的分布可以看出，豆瓣排名越高，电影的评分人数越多，评分也越高；评分人数大部分在100万以内，其中有一部电影，评分人数超过惊人的200万，可见这部电影有多热门，通过查找，可以得出这部电影是《肖申克的救赎》。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['肖申克的救赎'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['评分人数'] > 2000000]['电影名称'].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 尝试用pyecharts绘制散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts': '/nbextensions/echarts/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "    <div id=\"107ab51802f742d6a84cd85e39c9ff20\" style=\"width:980px;height:500px;\"></div>\n",
       "\n",
       "\n",
       "<script>\n",
       "    require(['echarts'], function(echarts) {\n",
       "        \n",
       "var myChart_107ab51802f742d6a84cd85e39c9ff20 = echarts.init(document.getElementById('107ab51802f742d6a84cd85e39c9ff20'), 'light', {renderer: 'canvas'});\n",
       "\n",
       "var option_107ab51802f742d6a84cd85e39c9ff20 = {\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u8c46\\u74e3\\u8bc4\\u5206&\\u8bc4\\u5206\\u4eba\\u6570\\u60c5\\u51b5\\u5206\\u5e03\",\n",
       "            \"left\": \"center\",\n",
       "            \"top\": \"auto\",\n",
       "            \"textStyle\": {\n",
       "                \"fontSize\": 18\n",
       "            },\n",
       "            \"subtextStyle\": {\n",
       "                \"fontSize\": 12\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"toolbox\": {\n",
       "        \"show\": true,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"left\": \"95%\",\n",
       "        \"top\": \"center\",\n",
       "        \"feature\": {\n",
       "            \"saveAsImage\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"save as image\"\n",
       "            },\n",
       "            \"restore\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"restore\"\n",
       "            },\n",
       "            \"dataView\": {\n",
       "                \"show\": true,\n",
       "                \"title\": \"data view\"\n",
       "            }\n",
       "        }\n",
       "    },\n",
       "    \"series_id\": 8742831,\n",
       "    \"tooltip\": {\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"backgroundColor\": \"rgba(50,50,50,0.7)\",\n",
       "        \"borderColor\": \"#333\",\n",
       "        \"borderWidth\": 0\n",
       "    },\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"scatter\",\n",
       "            \"symbol\": \"circle\",\n",
       "            \"symbolSize\": 8,\n",
       "            \"data\": [\n",
       "                [\n",
       "                    2100927,\n",
       "                    1\n",
       "                ],\n",
       "                [\n",
       "                    1557983,\n",
       "                    2\n",
       "                ],\n",
       "                [\n",
       "                    1587792,\n",
       "                    3\n",
       "                ],\n",
       "                [\n",
       "                    1775679,\n",
       "                    4\n",
       "                ],\n",
       "                [\n",
       "                    1540577,\n",
       "                    5\n",
       "                ],\n",
       "                [\n",
       "                    991942,\n",
       "                    6\n",
       "                ],\n",
       "                [\n",
       "                    1650918,\n",
       "                    7\n",
       "                ],\n",
       "                [\n",
       "                    807751,\n",
       "                    8\n",
       "                ],\n",
       "                [\n",
       "                    1514005,\n",
       "                    9\n",
       "                ],\n",
       "                [\n",
       "                    1054185,\n",
       "                    10\n",
       "                ],\n",
       "                [\n",
       "                    1262853,\n",
       "                    11\n",
       "                ],\n",
       "                [\n",
       "                    1136598,\n",
       "                    12\n",
       "                ],\n",
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       "        \"#2f4554\",\n",
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       "    ]\n",
       "};\n",
       "myChart_107ab51802f742d6a84cd85e39c9ff20.setOption(option_107ab51802f742d6a84cd85e39c9ff20);\n",
       "\n",
       "    });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<pyecharts.charts.scatter.Scatter at 0x2768e11a548>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter1 = pe.Scatter('豆瓣评分&评分人数情况分布', title_pos='center',\n",
    "                      height=500, width=980)\n",
    "\n",
    "scatter1.add('', x5, z, symbol_size=8, xaxis_name='评分人数',\n",
    "             yaxis_name='豆瓣排名', yaxis_name_gap=20, yaxis_name_pos='end')\n",
    "scatter1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts': '/nbextensions/echarts/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "    <div id=\"ff18d0358551469f93d7474c7ea4857f\" style=\"width:980px;height:500px;\"></div>\n",
       "\n",
       "\n",
       "<script>\n",
       "    require(['echarts'], function(echarts) {\n",
       "        \n",
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       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u8c46\\u74e3\\u8bc4\\u5206&\\u8bc4\\u5206\\u4eba\\u6570\\u60c5\\u51b5\\u5206\\u5e03\",\n",
       "            \"left\": \"center\",\n",
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     },
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   "source": [
    "scatter2 = pe.Scatter('豆瓣评分&评分人数情况分布', title_pos='center',\n",
    "                      height=500, width=980)\n",
    "\n",
    "scatter2.add('', x5, y5, symbol_size=8, xaxis_name='评分人数',\n",
    "             yaxis_name='豆瓣评分', yaxis_name_gap=20, yaxis_name_pos='end', yaxis_min=8)\n",
    "scatter2"
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   "source": [
    "电影评分在"
   ]
  },
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   "metadata": {},
   "source": [
    "## 总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "豆瓣top250电影分析这个项目，数据是自己爬虫抓取的，就250条数据，数据量不多，数据也比较干净，不需要做复杂的数据清洗，大部分图表是用 pyecharts 绘制的，这个库绘制出来的图表比较美观，并且可交互。      \n",
    "整个项目的流程是读取数据——数据概览——数据清洗——数据分析——得出结论。项目过程比较难的地方，是对电影的类型以及制片国家/地区的分析，因为一部电影有可能有多个类型标签，不能简单地把它归到某个具体的类型；一部电影也有可能是由多个国家/地区合作发行的，同样不能直接划分到某个国家/地区。  \n",
    "其实项目到分析电影的排名/评分/评分人数，用 matplotlib 出图就结束了，最后用pyecharts出图是想做到跟 matplotlib 一样的效果，用散点图的颜色代表分数，但是查了文档和教程，暂时找不到解决方案，所以就画两个图了，此处先放着，后续有解决方案了再改过来。"
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